Demosaicing techniques may reconstruct a full color image from incomplete color samples output from an image sensor overlaid with a color filter array (CFA). For example, demosaicing may be an important operation in a camera or imaging pipeline. Current techniques for performing demosaicing include spatial interpolation, frequency domain approaches, and example based demosaicing.
For example, spatial interpolation techniques may leverage the fact that the color channels are highly correlated to reconstruct the full color image. Frequency domain approaches such as frequency domain interpolation may use the known CFA sampling pattern to separate luminance and chrominance terms by applying appropriate filters. Example based demosaicing may include learning the relationship between the CFA pattern and surrounding pixels. For example, in example based demosaicing, a dictionary including pairs of image patches (each pair including an incomplete color patch in a pattern such as a Bayer pattern and a corresponding full resolution RGB patch or the like) may be provided. Incomplete color patch data from a sensor or the like may be used to reference the dictionary, which may provide a full resolution color patch.
Current techniques may perform well on most natural scenes. However, on high resolution natural images with varying hue and high saturation edges, performance degrades significantly due to erroneous constant-hue assumptions, chroma aliasing, and other limitations. Furthermore, improvement may be constantly sought even in contexts where current techniques may perform well. It is with respect to these and other considerations that the present improvements have been needed. Such improvements may become critical as the desire to attain high quality images becomes more widespread.